Deep learning-based autonomous vehicle control device, system including the same, and method thereof

ABSTRACT

A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on and claims the benefit of priority toKorean Patent Application No. 10-2017-0038405, filed on Mar. 27, 2017,in the Korean Intellectual Property Office, the disclosure of which isincorporated herein in its entirety by reference.

TECHNICAL FIELD

The present disclosure relates to a deep learning-based autonomousvehicle control device, a system including the same, and a methodthereof, and more particularly, to a technology for controlling anautonomous vehicle based on a predetermined expert rule to prevent anerror in determination of deep learning-based autonomous vehiclecontrol.

BACKGROUND

Recently, deep learning has drawn much attention for control of anautonomous vehicle as well as automatic recognition.

Such deep learning uses neural networks, and is being actively appliedto fields of image recognition and classification and is combined withreinforcement learning and the like to show high performance beyondhuman ability in specific fields. Based on enhanced learning capability,without preprocessing of sensors, deep learning is being applied to avariety of fields such as autonomous vehicles.

In deep learning, however, only learning results appear as output, andthus there is a difficulty in debugging with respect to erroneousoutput. In particular, there is no room for error in determination andcontrol of autonomous vehicles, and the impossibility of debugging indeep learning is considered a big risk.

SUMMARY

The present disclosure has been made to solve the above-mentionedproblems occurring in the prior art while advantages achieved by theprior art are maintained intact.

An aspect of the present disclosure provides a deep learning-basedautonomous vehicle control device, a system including the same, and amethod thereof, which are designed to control autonomous driving whiletaking expert rules into account in deep learning-based autonomousdriving control, thereby minimizing errors in determination of deeplearning-based autonomous vehicle control.

The technical problem to be solved by the present inventive concept isnot limited to the aforementioned problem, and any other technicalproblems not mentioned herein will be clearly understood from thefollowing description by those skilled in the art to which the presentdisclosure pertains.

According to an aspect of the present disclosure, a deep learning-basedautonomous vehicle control system includes: a processor determining anautonomous driving control based on deep learning, correcting an errorin determination of the deep learning-based autonomous driving controlbased on determination of an autonomous driving control based on apredetermined expert rule, and controlling an autonomous vehicle; and anon-transitory computer-readable storage medium storing data for thedetermination of the deep learning-based autonomous driving control,data for the determination of the expert rule-based autonomous drivingcontrol, and information about the error in the determination of thedeep learning-based autonomous driving control.

The processor may be further configured to: output a deep learning-basedautonomous driving control output value for the deep learning-basedautonomous driving control; output an expert rule-based autonomousdriving control output value based on the expert rule; and compare thedeep learning-based autonomous driving control output value with theexpert rule-based autonomous driving control output value and outputtinga final autonomous driving control output value depending on acomparison result.

The processor may output the deep learning-based autonomous drivingcontrol output value as the final autonomous driving control outputvalue when the deep learning-based autonomous driving control outputvalue and the expert rule-based autonomous driving control output valuematch.

The processor may output the expert rule-based autonomous drivingcontrol output value as the final autonomous driving control outputvalue unless the deep learning-based autonomous driving control outputvalue and the expert rule-based autonomous driving control output valuematch.

The expert rule may include at least one of limitations on steeringdirection with respect to free space, time to collision (TTC), degree ofchange in steering, degree of change in acceleration/deceleration, andlane departure.

When the deep learning-based autonomous driving control output valuecorresponds to a steering direction control in a direction in whichthere is no free space, the processor may correct the steering directioncontrol to a direction in which there is free space.

When the deep learning-based autonomous driving control output valuecorresponds to a steering or acceleration/deceleration output controlless than a predetermined minimum time to collision (TTC), the processormay stop the steering or acceleration/deceleration output control lessthan TTC.

When the deep learning-based autonomous driving control output valuecorresponds to a steering value greater than a predetermined steeringreference value, the processor may adjust the steering value to be lessthan the steering reference value.

When the deep learning-based autonomous driving control output valuecorresponds to an acceleration/deceleration value greater than apredetermined acceleration/deceleration reference value, the processormay adjust the acceleration/deceleration value to be less than theacceleration/deceleration reference value.

When the deep learning-based autonomous driving control output valuecorresponds to an output value for a steering control in a directiondeparting from a lane, the processor may stop the steering control.

The processor may further control the autonomous vehicle using the finalautonomous driving control output value that is output.

The deep learning-based autonomous driving control output value mayinclude at least one of a relative speed between a preceding vehicle anda subject vehicle, a relative distance between the preceding vehicle andthe subject vehicle, free space on a neighboring lane, a distance to aleft lane, a distance to a right lane, a lane number of a lane on whichthe subject vehicle is currently driving, and an angle between a laneand the subject vehicle.

The non-transitory computer-readable storage medium may include: a deeplearning storage storing a deep learning-based output control parameterfor the determination of the deep learning-based autonomous drivingcontrol; an expert rule storage storing the expert rule; and an errorstorage storing information about the error that is determined andcorrected by the processor.

According to another aspect of the present disclosure, a deeplearning-based autonomous vehicle control device includes a processconfigured to: output a deep learning-based autonomous driving controloutput value for a deep learning-based autonomous driving control;output an expert rule-based autonomous driving control output valuebased on an expert rule; compare the deep learning-based autonomousdriving control output value with the expert rule-based autonomousdriving control output value and outputting a final autonomous drivingcontrol output value depending on a comparison result; and control anautonomous vehicle using the final autonomous driving control outputvalue that is output.

The processor may output the deep learning-based autonomous drivingcontrol output value as the final autonomous driving control outputvalue when the deep learning-based autonomous driving control outputvalue and the expert rule-based autonomous driving control output valuematch.

The processor may output the expert rule-based autonomous drivingcontrol output value as the final autonomous driving control outputvalue unless the deep learning-based autonomous driving control outputvalue and the expert rule-based autonomous driving control output valuematch.

According to another aspect of the present disclosure, a deeplearning-based autonomous vehicle control method includes: outputting adeep learning-based autonomous driving control output value for a deeplearning-based autonomous driving control; outputting an expertrule-based autonomous driving control output value based on an expertrule; comparing the deep learning-based autonomous driving controloutput value with the expert rule-based autonomous driving controloutput value and outputting a final autonomous driving control outputvalue depending on a comparison result; and controlling an autonomousvehicle using the final autonomous driving control output value.

The outputting of the final autonomous driving control output value mayinclude outputting the deep learning-based autonomous driving controloutput value as the final autonomous driving control output value whenthe deep learning-based autonomous driving control output value and theexpert rule-based autonomous driving control output value match.

The outputting of the final autonomous driving control output value mayinclude outputting the expert rule-based autonomous driving controloutput value as the final autonomous driving control output value unlessthe deep learning-based autonomous driving control output value and theexpert rule-based autonomous driving control output value match.

The expert rule may include at least one of limitations on steeringdirection with respect to free space, time to collision (TTC), degree ofchange in steering, degree of change in acceleration/deceleration, andlane departure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentdisclosure will be more apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings:

FIG. 1 illustrates the configuration of a deep learning-based autonomousvehicle control system, according to exemplary embodiments of thepresent disclosure;

FIG. 2A illustrates an example of autonomous vehicle control when a deeplearning-based driving direction control output value and an expertrule-based driving direction control output value match, according toexemplary embodiments of the present disclosure;

FIG. 2B illustrates an example of autonomous vehicle control when a deeplearning-based driving direction control output value and an expertrule-based driving direction control output value do not match,according to exemplary embodiments of the present disclosure;

FIG. 3A illustrates an example of autonomous vehicle control when a deeplearning-based vehicle speed control output value and an expertrule-based vehicle speed control output value match, according toexemplary embodiments of the present disclosure;

FIG. 3B illustrates an example of autonomous vehicle control when a deeplearning-based steering control output value and an expert rule-basedsteering control output value do not match, according to exemplaryembodiments of the present disclosure;

FIG. 4 illustrates a flowchart of an expert rule setting method,according to exemplary embodiments of the present disclosure;

FIG. 5 illustrates a flowchart of an autonomous vehicle control method,according to exemplary embodiments of the present disclosure;

FIG. 6 illustrates a table showing expert rules for autonomous vehiclecontrol, according to exemplary embodiments of the present disclosure;and

FIG. 7 illustrates the configuration of a computing system by which anautonomous vehicle control method according to exemplary embodiments ofthe present disclosure is executed.

DETAILED DESCRIPTION

Hereinafter, exemplary embodiments of the present disclosure will bedescribed in detail with reference to the accompanying drawings. In thedrawings, the same reference numerals will be used throughout todesignate the same or equivalent elements. In addition, a detaileddescription of a related known function or configuration will be ruledout in order not to unnecessarily obscure the gist of the presentdisclosure.

Terms such as first, second, A, B, (a), and (b) may be used to describethe elements in exemplary embodiments of the present disclosure. Theseterms are only used to distinguish one element from another element, andthe intrinsic features, sequence or order, and the like of thecorresponding elements are not limited by the terms. Unless otherwisedefined, all terms used herein, including technical or scientific terms,have the same meanings as those generally understood by those withordinary knowledge in the field of art to which the present disclosurebelongs. Such terms as those defined in a generally used dictionary areto be interpreted as having meanings equal to the contextual meanings inthe relevant field of art, and are not to be interpreted as having idealor excessively formal meanings unless clearly defined as having such inthe present application.

Hereinafter, exemplary embodiments of the present disclosure will bedescribed in detail with reference to FIGS. 1 to 7.

FIG. 1 illustrates the configuration of a deep learning-based autonomousvehicle control system, according to exemplary embodiments of thepresent disclosure.

The deep learning-based autonomous vehicle control system, according tothe exemplary embodiments of the present disclosure, includes aprocessor 100 and a non-transitory computer-readable storage medium 200.

When the system determines the autonomous driving control of anautonomous vehicle based on deep learning, the processor 100 may correctan error in determination of the deep learning-based autonomous drivingcontrol on the basis of determination of autonomous driving controlbased on a predetermined expert rule to thereby control the autonomousvehicle. In other words, when performing the deep learning-basedautonomous driving control of the autonomous vehicle, the processor 100may compare a deep learning-based autonomous vehicle control outputvalue with an expert rule-based determination value and prioritize theexpert rule-based determination value when the deep learning-basedautonomous vehicle control output value and the expert rule-baseddetermination value do not match, thereby controlling the autonomousvehicle.

To this end, the processor 100 includes a deep learning processor 110,an expert rule determination processor 120, a deep learning errordetermination processor 130, and an autonomous driving control processor140. The processor 100 may be implemented by a combination of hardwareand software installed on the hardware and programmed to execute apredetermined operation. The hardware may include at least one processorand a memory.

In detail, the various embodiments disclosed herein, includingembodiments of the deep learning-based autonomous vehicle control systemand/or elements thereof, can be implemented using one or more processorscoupled to a memory (or other non-transitory machine readable recordingmedium) storing computer-executable instructions for causing theprocessor(s) to perform the functions described above including thefunctions described. The processor 100 and/or elements thereof also canbe implemented using one or more processors coupled to a memory (orother non-transitory machine readable recording medium) storingcomputer-executable instructions for causing the processor(s) to performthe functions described above including the functions described inrelation to the deep learning processor 110, the expert ruledetermination processor 120, the deep learning error determinationprocessor 130, and the autonomous driving control processor 140.

The deep learning processor 110 may perform deep learning using datapreviously stored through artificial neural networks to output a controlsignal for autonomous vehicle control.

The deep learning processor 110 may previously set a deep learningarchitecture in the initial stage and set a deep learning-based outputcontrol parameter. Here, the setting of the deep learning architecturerefers to setting the number of hidden layers, the number ofconvolutional neural networks (CNNs), and the like, and the deeplearning-based output control parameter refers to an output controlvalue for autonomous vehicle control.

Table 1 below shows examples of the deep learning-based output controlparameter. Here, the deep learning-based output control parameter refersto a deep learning-based autonomous driving control output value.

TABLE 1 Output Parameters V_pre P_pre Free_Space Lane_Left Lane_RightLane_num Heading Details relative relative free space distance distancelane angle speed distance (available to left to right number of betweenbetween between driving lane lane currently lane preceding precedingspace) on driving and vehicle vehicle neighboring lane subject and andlane vehicle subject subject vehicle vehicle

Referring to table 1, the deep learning-based output control parameterincludes at least one of a relative speed between a preceding vehicleand a subject vehicle, a relative distance between the preceding vehicleand the subject vehicle, free space (available driving space) on aneighboring lane, a distance to a left lane, a distance to a right lane,a lane number of a lane on which the subject vehicle is currentlydriving, and an angle between a lane and the subject vehicle. However,the present inventive concept is not limited to the deep learning-basedoutput control parameters described in table 1, and all parametersavailable for deep learning-based recognition may be included.

The expert rule determination processor 120 may output an expertrule-based autonomous driving control output value based on apredetermined expert rule. Here, the expert rule includes at least oneof limitations on steering direction with respect to free space, time tocollision (TTC), the degree of change in steering, the degree of changein acceleration/deceleration, and lane departure, as illustrated in FIG.6. FIG. 6 illustrates a table showing expert rules for autonomousvehicle control, according to exemplary embodiments of the presentdisclosure.

When the deep learning-based autonomous driving control output value andthe expert rule-based autonomous driving control output value match, thedeep learning error determination processor 130 may output the deeplearning-based autonomous driving control output value as a finalautonomous driving control output value.

When the deep learning-based autonomous driving control output value andthe expert rule-based autonomous driving control output value do notmatch, the deep learning error determination processor 130 may outputthe expert rule-based autonomous driving control output value as a finalautonomous driving control output value.

Referring to FIG. 6, when the deep learning-based autonomous drivingcontrol output value corresponds to a steering direction control in adirection in which there is no free space, the deep learning errordetermination processor 130 may correct the steering direction controlto a direction in which there is free space. When the deeplearning-based autonomous driving control output value for controllingthe steering direction in a direction toward a space where there isanother vehicle or an obstacle, rather than the free space, is output,the steering control may be stopped or may be corrected to the steeringdirection in which there is free space, and thus, a collision with aneighboring obstacle or vehicle may be prevented.

In addition, when the deep learning-based autonomous driving controloutput value corresponds to a steering or acceleration/decelerationoutput control less than a predetermined minimum time to collision(TTC), the deep learning error determination processor 130 may stop thesteering or acceleration/deceleration output control less than TTC. Inother words, the minimum TIC refers to the Maginot line for preventing acollision, and when the minimum TTC is not satisfied, there may be arisk of collision. Thus, by preventing the steering control less thanthe predetermined minimum TTC, an accident or the like may be avoided.

In addition, when the deep learning-based autonomous driving controloutput value corresponds to a steering value greater than apredetermined steering reference value, the deep learning errordetermination processor 130 may adjust the steering value to be lessthan the steering reference value or may stop the steering control. Inother words, when the excessive steering value is instantaneouslyoutput, the subject vehicle may be tilted or the like, and thusstability in posture control may be degraded. Thus, when the deeplearning-based autonomous driving control output value corresponds tothe steering value greater than the predetermined steering referencevalue, the steering control may be stopped or the steering value may beadjusted to be less than the steering reference value.

When the deep learning-based autonomous driving control output valuecorresponds to an acceleration/deceleration value greater than apredetermined acceleration/deceleration reference value, the deeplearning error determination processor 130 may adjust theacceleration/deceleration value to be less than theacceleration/deceleration reference value or may stop theacceleration/deceleration control. In other words, the excessiveacceleration/deceleration value is instantaneously output, the postureof the subject vehicle may be unstable or passengers may feeluncomfortable. Thus, when the deep learning-based autonomous drivingcontrol output value exceeding the predeterminedacceleration/deceleration reference value is output, theacceleration/deceleration value may be adjusted to be less than theacceleration/deceleration reference value or theacceleration/deceleration control may be stopped.

When the deep learning-based autonomous driving control output valuecorresponds to an output value for a steering control in a directiondeparting from a lane, the deep learning error determination processor130 may stop the steering control. In other words, while the subjectvehicle is running on a last lane, i.e., a leftmost lane or a rightmostlane, if the deep learning-based autonomous driving control output valuefor a steering control in the direction departing from the lane (thedirection in which there is no lane) is output, the deep learning errordetermination processor 130 may recognize its lane number and preventthe steering control, and thus, an accident caused by the vehicledriving in the direction in which there is no lane may be avoided.

The autonomous driving control processor 140 may perform the autonomousvehicle control using the final autonomous driving control output valueoutput from the deep learning error determination processor 130.

The non-transitory computer-readable storage medium 200 may store theparameters for the deep learning-based autonomous driving control andthe expert rule-based autonomous driving control that are performed bythe processor 100, and may store a deep learning error that isdetermined when the determination of the expert rule-based autonomousdriving control is different from the determination of the deeplearning-based autonomous driving control.

To this end, the non-transitory computer-readable storage medium 200includes a deep learning storage 210, an expert rule storage 220, and anerror storage 230.

The deep learning storage 210 may store a deep learning-based outputcontrol parameter for determination of deep learning-based autonomousdriving control. For example, the deep learning storage 210 may storethe deep learning-based output control parameters as shown in table 1.

The expert rule storage 220 may store an expert rule. The expert ruleincludes at least one of limitations on steering direction with respectto free space, time to collision (TTC), the degree of change insteering, the degree of change in acceleration/deceleration, and lanedeparture.

The error storage 230 may store information about the deep learningerror that is determined and corrected by the deep learning errordetermination processor 130. By storing the deep learning errorinformation and applying the deep learning error information to the deeplearning-based autonomous driving control, reliability of the deeplearning-based autonomous vehicle control system may be improved.

FIG. 2A illustrates an example of autonomous vehicle control when a deeplearning-based driving direction control output value and an expertrule-based driving direction control output value match, according toexemplary embodiments of the present disclosure. Referring to FIG. 2A,when the deep learning processor 110 outputs a left turn control signal,and the expert rule determination processor 120 also outputs a left turncontrol signal, the deep learning error determination processor 130transmits the left turn control signal to the autonomous driving controlprocessor 140 by which the vehicle is controlled to turn left.

FIG. 2B illustrates an example of autonomous vehicle control when a deeplearning-based driving direction control output value and an expertrule-based driving direction control output value do not match,according to exemplary embodiments of the present disclosure. Referringto FIG. 2B, when the deep learning processor 110 outputs a left turncontrol signal, and the expert rule determination processor 120 outputsa right turn control signal, the output of the deep learning processor110 is different from the output of the expert rule determinationprocessor 120, and thus, the deep learning error determination processor130 transmits the right turn control signal that is output from theexpert rule determination processor 120 to the autonomous drivingcontrol processor 140 by which the vehicle is controlled to turn right,and the error storage 230 stores information that the output of the deeplearning processor 110 is different from the output of the expert ruledetermination processor 120.

FIG. 3A illustrates an example of autonomous vehicle control when a deeplearning-based vehicle speed control output value and an expertrule-based vehicle speed control output value match, according toexemplary embodiments of the present disclosure. Referring to FIG. 3A,when the deep learning processor 110 outputs a control signal for thirdstage deceleration (deceleration −3), and the expert rule determinationprocessor 120 outputs a control signal within fifth stage deceleration(deceleration>−5), the third stage deceleration control of the deeplearning processor 110 is within the fifth stage deceleration control ofthe expert rule determination processor 120, and thus, the deep learningerror determination processor 130 transmits the third stage deceleration(deceleration −3) control signal that is output from the deep learningprocessor 110 to the autonomous driving control processor 140 by whichthe vehicle is controlled to perform the third stage deceleration.

FIG. 3B illustrates an example of autonomous vehicle control when a deeplearning-based steering control output value and an expert rule-basedsteering control output value do not match, according to exemplaryembodiments of the present disclosure. Referring to FIG. 3B, when thedeep learning processor 110 outputs a control signal for + (plus)steering direction, and the expert rule determination processor 120outputs a control signal for − (minus) steering direction, the output ofthe deep learning processor 110 is different from the output of theexpert rule determination processor 120, and thus, the deep learningerror determination processor 130 transmits the control signal for −steering direction that is output from the expert rule determinationprocessor 120 to the autonomous driving control processor 140 by whichthe vehicle is controlled to drive in the − steering direction, and theerror storage 230 stores information that the output of the deeplearning processor 110 is different from the output of the expert ruledetermination processor 120.

Hereinafter, an expert rule setting method, according to exemplaryembodiments of the present disclosure, will be described with referenceto FIG. 4. An autonomous vehicle control system, according to exemplaryembodiments of the present disclosure, may set a deep learningarchitecture in S110, and may set a deep learning-based output controlparameter in S120. Here, the setting of the deep learning architecturerefers to setting the number of hidden layers, the number ofconvolutional neural networks (CNNs), and the like, and the outputcontrol parameter refers to an output control value for autonomousvehicle control. Then, the autonomous vehicle control system may set anexpert rule selected by a user or provided by an expert in S130.

Here, the process in FIG. 4 of setting the deep learning architecture,the output control parameter, and the expert rule may be initiallyperformed prior to deep learning-based autonomous driving.

Hereinafter, an autonomous vehicle control method, according toexemplary embodiments of the present disclosure, will be described withreference to FIG. 5.

First of all, while a deep learning-based autonomous vehicle controlsystem is automatically driving based on deep learning in S210, thesystem may compare a deep learning-based autonomous driving controloutput value with an expert rule-based autonomous driving control outputvalue, and when the deep learning-based autonomous driving controloutput value and the expert rule-based autonomous driving control outputvalue do not match, the system may determine that the deeplearning-based autonomous driving control output value violates theexpert rule in S220.

When the deep learning-based autonomous driving control output valueviolates the expert rule, the autonomous driving may be performed on thebasis of the expert rule-based autonomous driving control output value,irrespective of the deep learning-based autonomous driving controloutput value, in S230.

As described above, by applying the predetermined expert rule to thedeep learning-based autonomous driving, an error that may occur indetermination of the deep learning-based autonomous driving control maybe minimized, and by preventing the deep learning-based erroneousdetermination from continuously being learned as a correct determinationand updating information about the erroneous determination, thereliability of the deep learning-based autonomous vehicle control systemmay be improved.

FIG. 7 illustrates the configuration of a computing system by which anautonomous vehicle control method according to exemplary embodiments ofthe present disclosure is executed.

Referring to FIG. 7, a computing system 1000 includes at least oneprocessor 1100, a bus 1200, a memory 1300, a user interface input device1400, a user interface output device 1500, a storage 1600, and a networkinterface 1700, wherein these elements are connected through the bus1200.

The processor 1100 may be a central processing unit (CPU) or asemiconductor device processing commands stored in the memory 1300and/or the storage 1600. The memory 1300 and the storage 1600 includevarious types of volatile or non-volatile storage media. For example,the memory 1300 includes a read only memory (ROM) and a random accessmemory (RAM).

Therefore, the steps of the method or algorithm described in connectionwith the exemplary embodiments disclosed herein may be implementeddirectly by a hardware module or a software module that is executed bythe processor 1100, or a combination of both. The software module mayreside in storage media, i.e., the memory 1300 and/or the storage 1600,such as RAM, a flash memory, ROM, an erasable programmable read-onlymemory (EPROM), an electrically erasable programmable read-only memory(EEPROM), a register, a hard disk, a removable disk, and a CD-ROM.

The exemplary storage media may be coupled to the processor 1100, suchthat the processor 1100 may read information from the storage media andwrite information to the storage media. Alternatively, the storage mediamay be integrated with the processor 1100. The processor 1100 and thestorage media may reside in an application specific integrated circuit(ASIC). The ASIC may reside in a user terminal. Alternatively, theprocessor 1100 and the storage media may reside as individual componentsin a user terminal.

As set forth above, by applying a predetermined expert rule to deeplearning-based autonomous vehicle control, an error in deeplearning-based autonomous vehicle control may be prevented, and byupdating information about the error, the reliability of the deeplearning-based autonomous vehicle control system may be improved, andthus safe driving may be achieved to enhance the user convenience.

Hereinabove, although the present disclosure has been described withreference to exemplary embodiments and the accompanying drawings, thepresent disclosure is not limited thereto, but may be variously modifiedand altered by those skilled in the art to which the present disclosurepertains without departing from the spirit and scope of the presentdisclosure claimed in the following claims.

What is claimed is:
 1. A deep learning-based autonomous vehicle controlsystem, comprising: a processor configured to determine an autonomousdriving control based on deep learning, to correct an error indetermination of a deep learning-based autonomous driving control basedon determination of an autonomous driving control based on apredetermined expert rule, and to control an autonomous vehicle; and anon-transitory computer-readable storage medium storing data for thedetermination of the deep learning-based autonomous driving control,data for the determination of the expert rule-based autonomous drivingcontrol, and information about the error in the determination of thedeep learning-based autonomous driving control.
 2. The deeplearning-based autonomous vehicle control system according to claim 1,wherein the processor is further configured to: output a deeplearning-based autonomous driving control output value for the deeplearning-based autonomous driving control; output an expert rule-basedautonomous driving control output value based on the expert rule; andcompare the deep learning-based autonomous driving control output valuewith the expert rule-based autonomous driving control output value andoutputting a final autonomous driving control output value depending ona comparison result.
 3. The deep learning-based autonomous vehiclecontrol system according to claim 2, wherein the processor outputs thedeep learning-based autonomous driving control output value as the finalautonomous driving control output value when the deep learning-basedautonomous driving control output value and the expert rule-basedautonomous driving control output value match.
 4. The deeplearning-based autonomous vehicle control system according to claim 3,wherein the processor outputs the expert rule-based autonomous drivingcontrol output value as the final autonomous driving control outputvalue unless the deep learning-based autonomous driving control outputvalue and the expert rule-based autonomous driving control output valuematch.
 5. The deep learning-based autonomous vehicle control systemaccording to claim 3, wherein the predetermined expert rule includes atleast one of limitations on a steering direction with respect to a freespace, a time to collision (TTC), a degree of change in steering, adegree of change in acceleration/deceleration, and a lane departure. 6.The deep learning-based autonomous vehicle control system according toclaim 3, wherein when the deep learning-based an autonomous drivingcontrol output value corresponds to a steering direction control in adirection in which there is no free space, the processor corrects thesteering direction control to a direction in which there is a freespace.
 7. The deep learning-based autonomous vehicle control systemaccording to claim 3, wherein when the deep learning-based an autonomousdriving control output value corresponds to a steering oracceleration/deceleration output control less than a predeterminedminimum time to collision (TTC), the processor stops the steering oracceleration/deceleration output control less than the predeterminedminimum TTC.
 8. The deep learning-based autonomous vehicle controlsystem according to claim 3, wherein when the deep learning-basedautonomous driving control output value corresponds to a steering valuegreater than a predetermined steering reference value, the processoradjusts the steering value to be less than the steering reference value.9. The deep learning-based autonomous vehicle control system accordingto claim 3, wherein when the deep learning-based autonomous drivingcontrol output value corresponds to an acceleration/deceleration valuegreater than a acceleration/deceleration reference value, the processoradjusts the acceleration/deceleration value to be less than theacceleration/deceleration reference value.
 10. The deep learning-basedautonomous vehicle control system according to claim 3, wherein when thedeep learning-based autonomous driving control output value correspondsto an output value for a steering control in a direction departing froma lane, the processor stops the steering control.
 11. The deeplearning-based autonomous vehicle control system according to claim 3,wherein the processor further configured to control the autonomousvehicle using the final autonomous driving control output value.
 12. Thedeep learning-based autonomous vehicle control system according to claim3, wherein the deep learning-based autonomous driving control outputvalue includes at least one of a relative speed between a precedingvehicle and a subject vehicle, a relative distance between the precedingvehicle and the subject vehicle, a free space on a neighboring lane, adistance to a left lane, a distance to a right lane, a lane number of alane on which the subject vehicle is currently driving, and an anglebetween a lane and the subject vehicle.
 13. The deep learning-basedautonomous vehicle control system according to claim 3, wherein thenon-transitory computer-readable storage medium comprises: a deeplearning storage storing a deep learning-based output control parameterfor the determination of the deep learning-based autonomous drivingcontrol; an expert rule storage storing the predetermined expert rule;and an error storage storing the information about the error that isdetermined and corrected by the processor.
 14. A deep learning-basedautonomous vehicle control device, comprising a processor configured to:output a deep learning-based autonomous driving control output value fora deep learning-based autonomous driving control; output an expertrule-based autonomous driving control output value based on an expertrule; compare the deep learning-based autonomous driving control outputvalue with the expert rule-based autonomous driving control output valueand outputting a final autonomous driving control output value dependingon a comparison result; and control an autonomous vehicle using thefinal autonomous driving control output value.
 15. The deeplearning-based autonomous vehicle control device according to claim 14,wherein the processor outputs the deep learning-based autonomous drivingcontrol output value as the final autonomous driving control outputvalue when the deep learning-based autonomous driving control outputvalue and the expert rule-based autonomous driving control output valuematch.
 16. The deep learning-based autonomous vehicle control deviceaccording to claim 15, wherein the processor outputs the expertrule-based autonomous driving control output value as the finalautonomous driving control output value unless the deep learning-basedautonomous driving control output value and the expert rule-basedautonomous driving control output value match.
 17. A deep learning-basedautonomous vehicle control method, comprising steps of: outputting, by aprocessor, a deep learning-based autonomous driving control output valuefor a deep learning-based autonomous driving control; outputting, by theprocessor, an expert rule-based autonomous driving control output valuebased on an expert rule; comparing, by the processor, the deeplearning-based autonomous driving control output value with the expertrule-based autonomous driving control output value and outputting afinal autonomous driving control output value depending on a comparisonresult; and controlling, by the processor, an autonomous vehicle usingthe final autonomous driving control output value.
 18. The deeplearning-based autonomous vehicle control method according to claim 17,wherein the step of outputting the final autonomous driving controloutput value comprises outputting the deep learning-based autonomousdriving control output value as the final autonomous driving controloutput value when the deep learning-based autonomous driving controloutput value and the expert rule-based autonomous driving control outputvalue match.
 19. The deep learning-based autonomous vehicle controlmethod according to claim 18, wherein the step of outputting the finalautonomous driving control output value comprises outputting the expertrule-based autonomous driving control output value as the finalautonomous driving control output value unless the deep learning-basedautonomous driving control output value and the expert rule-basedautonomous driving control output value match.
 20. The deeplearning-based autonomous vehicle control method according to claim 17,wherein the expert rule includes at least one of limitations on steeringdirection with respect to a free space, a time to collision (TTC), adegree of change in steering, a degree of change inacceleration/deceleration, and a lane departure.